The paper introduces selective linkage precision, a metric for evaluating how accurately vision‑language models can match de‑identified chest radiographs to their reports when only certain attributes (such as sex, age, projection, or acquisition year) are available. Experiments on eight public VLMs and three chest‑radiograph datasets show that filtering by these attributes dramatically improves precision—from a baseline of 1.98% to 67.68% with sex, age, and projection, and up to 99.05% when acquisition year is added—while keeping the candidate set manageable. The study demonstrates that this approach can be applied without retraining the models and may guide hospitals in deciding which metadata to release.
arXiv:2606. 02276v1 Announce Type: cross Abstract: Vision-language models (VLMs) trained on paired chest radiographs and radiology reports learn a shared embedding space that can preserve instance-level image-report correspondence.
arXiv:2606. 17710v1 Announce Type: cross Abstract: Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image.
arXiv:2608. 07550v1 Announce Type: cross Abstract: Vision-language models return structured chest-radiograph findings through interfaces exposing no confidence score, so a receiving institution cannot read off how far to trust an individual judgment.
arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.
The study investigates when adding zero‑shot pseudo‑labels from vision‑language models (VLMs) to small expert‑labelled medical imaging datasets ceases to improve, and may even degrade, classifier performance. Across three benchmarks—PatchCamelyon, ISIC 2019, and NIH ChestX‑ray14—the crossover point occurs after roughly 20–50, 100, and 250–500 expert labels, respectively, with additional pseudo‑labels reducing macro‑AUC by up to 0.14 points. The analysis shows that the VLM’s labeling quality, rather than the student model size, limits performance, and a simple bias‑variance rule can predict the benefit of pseudo‑labels in most settings.
CheXPoT is a post‑hoc visual verification framework that refines vision‑language model (VLM) generated chest radiograph reports by decomposing them into predicates, grounding each against DenseNet‑121 and Grad‑CAM evidence, and removing unsupported findings. On a held‑out set of 4,145 images, CheXPoT significantly improves precision and F1 scores across multiple CheXpert classes, boosting micro‑average precision from 19.2 % to 46.8 % and micro‑average F1 from 23.0 % to 49.4 %. The method also enhances performance for other VLM generators such as LLaVA‑Med and MAIRA‑2, demonstrating that inexpensive visual verification can correct hallucinations and improve report quality.
The study evaluates the robustness of medical vision‑language models for tuberculosis screening on chest X‑rays by testing them across multiple datasets, prompts, and evaluation settings. Three specialized models (BioMedCLIP, CheXficient, MedSigLIP) and a general OpenCLIP model were audited on 12,200 images, producing 244,000 model–image–prompt scores. Results show that no model consistently outperforms others across all cohorts and reliability criteria, with prompt changes and control group composition significantly affecting AUROC, and that high training‑set performance does not reliably transfer to external cohorts.
The study evaluated ten chest X‑ray AI classification models on 12 datasets from six countries, focusing on image‑level classification and measuring specificity at 95% sensitivity. MAVL and MedKLIP achieved the highest specificity (≈0.37) but still produced many false positives, with performance dropping on institutional cohorts compared to public benchmarks. No single model excelled across all findings, countries, or lesion types, highlighting limited generalisability.
arXiv:2609.01470v1 Announce Type: new Abstract: As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language...
Med-AR introduces two autoregressive vision‑language models, Med‑AR‑8B and Med‑AR‑2B, pretrained on structured radiology reports, abnormality‑focused text, and region annotations to address long‑tailed chest X‑ray classification. The models outperform existing contrastive, self‑supervised, and supervised encoders—including Med‑CLIP, CheXFound, EVA‑Base, ARK, and BioViL‑T—across PadChest, MIMIC‑CXR, and CheXpert, achieving higher mean AUROC and AUPRC for head, medium, and tail findings and lower excess area under the risk‑coverage curve. Med‑AR also demonstrates improved selective‑prediction performance, with Med‑AR‑8B raising tail‑label mean AUPRC on MIMIC‑CXR from 0.1033 to 0.1441 and Med‑AR‑2B delivering the strongest discrimination on PadChest.
arXiv:2606.15910v3 Announce Type: replace Abstract: An answer from a vision-language model (VLM) to a medical image question can be accepted without review only under a threshold bounding the error o...
arXiv:2607. 25589v1 Announce Type: cross Abstract: Medical-imaging AI benchmarks combine datasets, DICOM rendering, prompts, provider APIs, automated labels, statistical code, manuscripts, and repository releases.